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SWIM Water Extent - Sentinel-1/2 - Daily

SWIM Water Extent is a global surface water product at 10 m pixel spacing based on Sentinel-1/2 data. The collection contains binary layers indicating open surface water for each Sentinel-1/2 scene. Clouds and cloud shadows are removed using ukis-csmask (see: https://github.com/dlr-eoc/ukis-csmask ) and are represented as NoData. The water extent extraction is based on convolutional neural networks (CNN). For further information, please see the following publications: https://doi.org/10.1016/j.rse.2019.05.022 and https://doi.org/10.3390/rs11192330

WMS SL Sentinel-2 TCI - Sentinel-2 TCI 2018

Sentinel-2 Echtfarbenbild (TCI), Kombination der Spektralkanäle B4 (rot), B3 (grün) und B2 (blau), räumliche Auflösung 10 m (2019):Dieser Layer visualisiert das Sentinel-2 Echtfarbenbild (TCI) des Jahr 2018.

WMS SL Sentinel-2 TCI - Sentinel-2 TCI 2020

WMS SL Sentinel-2 CIR - Sentinel-2 CIR 2021

Sentinel-2 Falschfarbenbild (ColoredInfraRed), Kombination der Spektralkanäle B8 (rot), B4 (grün) und B3 (blau), räumliche Auflösung 10 m (2019):Dieser Layer visualisiert die Sentinel-2 Falschfarbenbilder(CIR) des Jahr 2021.

IceLines - Sentinel-1 - Antarctica

IceLines (Ice Shelf and Glacier Front Time Series) is an automated calving front monitoring service providing monthly ice shelf front time series of major Antarctic ice shelves. The provided time series allows to discover the dynamics of ice shelf front changes and calving events. The front positions are automatically derived from Sentinel-1 data based on a deep neuronal network called HED-U-Net. The time series covers the timespan 2014 to today (partly limited due to Sentinel-1 data availability). Incorrectly extracted fronts are truncated which might lead to gaps in the time series especially between December to March due to strong surface melt. Annual averages are calculated based on the extracted monthly fronts (excluding the summer months) and provide more robust results due to temporal aggregation

CropTypes - Crop Type Maps for Germany - Yearly, 10m

This raster dataset shows the main type of crop grown on each field in Germany each year. Crop types and crop rotation are of great economic importance and have a strong influence on the functions of arable land and ecology. Information on the crops grown is therefore important for many environmental and agricultural policy issues. With the help of satellite remote sensing, the crops grown can be recorded uniformly for whole Germany. Based on Sentinel-1 and Sentinel-2 time series as well as LPIS data from some Federal States of Germany, 18 different crops or crop groups were mapped per pixel with 10 m resolution for Germany on an annual basis since 2018. These data sets enable a comparison of arable land use between years and the derivation of crop rotations on individual fields. More details and the underlying (in the meantime slightly updated) methodology can be found in Asam et al. 2022. This raster dataset shows the main type of crop grown on each field in Germany each year. Crop types and crop rotation are of great economic importance and have a strong influence on the functions of arable land and ecology. Information on the crops grown is therefore important for many environmental and agricultural policy issues. With the help of satellite remote sensing, the crops grown can be recorded uniformly for whole Germany. Based on Sentinel-1 and Sentinel-2 time series as well as LPIS data from some Federal States of Germany, 18 different crops or crop groups were mapped per pixel with 10 m resolution for Germany on an annual basis since 2017. These data sets enable a comparison of arable land use between years and the derivation of crop rotations on individual fields. More details and the underlying (in the meantime slightly updated) methodology can be found in Asam et al. 2022.

World Settlement Footprint (WSF) 2019 - Sentinel-1/2 - Global

The World Settlement Footprint (WSF) 2019 is a 10m resolution binary mask outlining the extent of human settlements globally derived by means of 2019 multitemporal Sentinel-1 (S1) and Sentinel-2 (S2) imagery. Based on the hypothesis that settlements generally show a more stable behavior with respect to most land-cover classes, temporal statistics are calculated for both S1- and S2-based indices. In particular, a comprehensive analysis has been performed by exploiting a number of reference building outlines to identify the most suitable set of temporal features (ultimately including 6 from S1 and 25 from S2). Training points for the settlement and non-settlement class are then generated by thresholding specific features, which varies depending on the 30 climate types of the well-established Köppen Geiger scheme. Next, binary classification based on Random Forest is applied and, finally, a dedicated post-processing is performed where ancillary datasets are employed to further reduce omission and commission errors. Here, the whole classification process has been entirely carried out within the Google Earth Engine platform. To assess the high accuracy and reliability of the WSF2019, two independent crowd-sourcing-based validation exercises have been carried out with the support of Google and Mapswipe, respectively, where overall 1M reference labels have been collected based photointerpretation of very high-resolution optical imagery.

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